OddCI

A growing number of very simple parallel applications can benefit from the availability of very large computing resource pools that are exploited in an opportunistic way to considerably speed up the processing of these applications. In particular, many applications can scale out to take advantage of the availability of thousands, or even millions, of processors that can be simultaneously used. Some large-scale distributed computing infrastructures (DCI) have already been successfully assembled to this end. However, these DCI are normally designed to cater to specific kinds of applications, and their assemblage is not only costly, but also time consuming. We propose a novel architecture, named OddCI (On-demand DCI), for the cheap, fast and on-demand instantiation and dismantle of very large DCI. These characteristics allow application users to dynamically commission DCI that can cost-effectively be customized to their particular needs, and disposed after being used. Our approach leverages on broadcast communication as an efficient mechanism to enable aggregation of geographically distributed computing resources, including millions of non-traditional processing devices such as mobile phones, and digital television (DTV) receivers. We discuss how the proposed architecture can be implemented on top of a network of DTV receivers, providing evidences of both the technical, as well as the operational feasibility of the OddCI architecture.

In recent years Digital TV receivers (DTV) and Set- Top Boxes (STB) have been advancing in terms of new functionalities they offer to the end-users. This has mainly been triggered by the appearance of new multimedia formats and applications needed to be adequately handled by the DTV/STB. In such complex system within the environment consisting of wide range of non-interactive as well as interactive applications and services for television, internet, gamming, healthcare, surveillance, etc., efficient and reliable DTV functional verification becomes highly necessary and challenging task. Since efficient DTV testing is considered as one of the most important issues for the reduced time-to-market, its optimization is of crucial importance to the DTV manufacturing process. Having in mind the end-users as the main target of the DTV, optimal test generation process must also include estimated usage-profiles in addition to the pre-defined DTV designed specification. This is especially important considering new emerging multimedia technologies for DTV which are not yet fully standardized and evaluated, and which exact employment (from the point of end-users) is not known in advance. In this paper we propose a novel approach for user driven test-case generation that would enable adequate and efficient testing of the DTV/STB in different development stages before reaching the market, i.e., the end-users. The proposed user driven approach provides optimal test-case generation in terms of both test execution time and adequacy according to the real end-user DTV functional DTV operation and quality of experience (QoE). This is obtained by incorporating a novel design and selection scheme for test-cases generation based on the modeled end-users most required functional operations on the DTV/STB under test and its expectations of it.

The effects of radiofrequency exposure on cognition: A systematic review and meta-analysis of human observational studies

Paper gives a software solution for Smart-Phone application to be used as a remote controller for digital TV receiver. The novel remote controller is using IP (Internet Protocol) based communication for remote communication with digital TV receiver.

Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever growing size of knowledge graphs requires computationally efficient algorithms capable of scaling to graphs with millions of nodes and billions of edges. This paper presents DGL-KE, an open-source package to efficiently compute knowledge graph embeddings. DGL-KE introduces various novel optimizations that accelerate training on knowledge graphs with millions of nodes and billions of edges using multi-processing, multi-GPU, and distributed parallelism. These optimizations are designed to increase data locality, reduce communication overhead, overlap computations with memory accesses, and achieve high operation efficiency. Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine. These results represent a 2× ~ 5× speedup over the best competing approaches. DGL-KE is available on https://github.com/awslabs/dgl-ke.

The following paper discusses several aspects connected with the wind farms’ impact on radiocommunication systems. The first part of this article is filled with the analysis of the ITU-R BT.1893 model, which was originally created for the analysis of the interaction between the wind turbines and digital TV receivers in the UHF band. A measurement campaign carried out by the authors confirmed that this model is also applicable for the lower, maritime VHF band. Utilizing the software implementation of this model, the authors conducted a thorough simulation analysis of the wind turbines’ influence on radio systems working in both VHF and UHF bands. The results of these simulations are presented and discussed in the second part of the paper.

We propose the methodology of field strength prediction for a digital television (DTV) receiver by virtue of Rec. ITU-R P.1546. The curves shown in this Rec. represent the point-to area field strength for 1kW effective radiated power in the 30 MHz ∼ 3000 MHz. Some computed results are illustrated here from the formulation of field strength for DTV receiver. It was shown that the error of field strength value for dense urban area (R = 30 m) is in the range of about 6.8 ∼ 11.5 % compared with the Okumura-Hata model under some conditions. In addition comparison of service distance between analog and digital TV receivers has been also illustrated. The presented method provides the predicted values of field strength for TV receiver whether transmission quality can be assured or not, and it can be also used to get the protection ratio or separated distance for frequency sharing in the same or adjacent band.

<p dir="ltr"><b>Background</b></p><p dir="ltr">A credible public health survey or surveillance program is judged by its ability to measure intended outcomes accurately and cost effectively. However, in the pursuit of operational efficiency, such programs risk introducing bias in their estimates. This thesis examines several key potential sources of bias in public health surveys, focusing on three main domains: the characteristics and selection of respondents paying attention to the hard-to-reach late respondents, the use of mobile phone technology for data collection, and the adaptation of standardized measurement tools-specifically the Framingham Risk Score (FRS) and the Finnish Diabetes Risk Score (FINDRISC). The tools are internationally recognized for estimating the 10-year risk of cardiovascular disease and type 2 diabetes, respectively. By exploring these domains, this work aims to highlight critical methodological considerations that survey designers and implementers must consider minimizing measurement bias and enhance data validity.</p><p dir="ltr"><b>Methods</b></p><p dir="ltr">The study was conducted within the Rakai Community Cohort Study (RCCS), a long-standing surveillance program in south-central Uganda. Covering 34 diverse communities, the RCCS focuses on HIV and other health priorities. In each round, 70-80% respond on time, while late respondents require extra effort from the researchers to track them. Study I examined whether late respondents differ from early ones and if their (late respondents) exclusion could bias results. We conducted descriptive analyses and compared these groups using Chi-square tests. Multivariable logistic regression was used to adjust for confounders, reporting adjusted odds ratios (AORs) with 95% confidence intervals. We also modelled interactions with age to estimate marginal predictive means (MPMs), illustrating how the likelihood of late response varies across age groups. The results are reported in constituent manuscript I. The use of mobile phones for population-based surveys is an ongoing global discussion, particularly in resource-limited settings. We leveraged RCCS Rounds 19 and 18 data, collected around the COVID-19 period, and data from a concurrent mobile phone-based surveillance in the same population to compare participation rates by survey modality (face-to-face vs. mobile phone surveys), we assessed phone access, and characterized the differences between respondents reached and missed by each method. The primary independent variable was interviewing modality, with adjustments made for sex, age, community type, HIV status, and other covariates. Robust standard errors accounted for clustering at the individual level. We also examined longitudinal trends in mobile phone ownership, including the frequency of mobile phone number change, the time to change of numbers and multiple SIM ownership. Phone number change was defined as the complete replacement of previously recorded numbers between survey rounds, excluding partial changes. Rates and associated factors were estimated using Poisson regression with generalized estimating equations (GEE), while time to number change was assessed using the Andersen-Gill Cox model to account for repeated measurements. The results are reported in studies II (face-to-face vs mobile phone surveys) and study III (mobile phone ownership, numbers change and time to change analyses) We conducted qualitative exit interviews to assess participants' experiences with the FRS and FINDRISC risk score tools. Interviews were held on the same day, immediately after participants completed the questionnaires and measurement procedures. The analysis, based on 15 interviews, followed an iterative approach. Thematic coding was used to identify and summarize key themes that we report in constituent study IV.</p><p dir="ltr"><b>Results</b></p><p dir="ltr">Overall, 68,263 participants were included in the late respondent analysis. Men were 22% times more likely to be late respondents than women (23% vs. 19%, respectively). This was true for younger participants 15-19 years old relative to 40+years old participants. Compared to the unemployed, participants engaged in formal employment followed by those engaged in fishing were more likely to be late respondents (28% and 27% respectively). When comparing response times between people living with HIV and those who are HIV-negative, no significant difference was observed (AOR=1.06; 95% C.I .: 0.99, 1.13). In women, HIV status had no effect on the marginal predictive mean (MPM) of being late, which declined with age in both groups. Among young men, MPM of being a late respondent was significantly higher in those with HIV but decreased with age. Conversely, in HIV- negative men, the MPM increased with age. In study II, we observed 90% access to mobile phones, with lower access among older people, and people living with HIV. When including only individuals who participated in the previous RCCS survey round, participation in the face-to-face survey (82%) was higher than participation in the mobile phone survey (75%, p < . 001). Survey participation was higher among people living with HIV compared to HIV-negative individuals (84% vs 82%, p< . 001) in the face-to-face survey. In study III we assessed mobile phone ownership. A total of 97,034 participants were included in this analysis. Overall, 61.8% reported owning a phone. Mobile phone ownership increased by 33% over the study period, from 51% in 2010 to 68% in 2020 (p<0.001). Ownership was higher among men. There was no difference in phone ownership by HIV status (adjPR=1.01, 95% CI 0.97-1.05). About 75% of participants who reported more than one sexual partner in the past 12 months owned a mobile phone, compared to 58% who reported zero or one partner in the same period (p<0.001). Phone number change is as high as 15-19/100 person years and consistent across the ten years of observation.</p><p dir="ltr">When we examined participants' experiences with FRS and FINDRISC, we found that overall, the tools were clear and acceptable in this setting. Participants were comfortable with having their weights, waists, heights and other measurements taken if it was by a health worker. There were a few challenges with interpretation and measurement of physical activity, and dietary practice questions given the seasonal variations in their availability in the community.</p><p dir="ltr"><b>Conclusion</b></p><p dir="ltr">The studies reveal that late respondents are different and cannot be ignored therefore means to speed up their participation should be devised. The use of mobile phones is feasible, there are some differentials in those who are missed by phone surveys (especially men). Meanwhile, mobile phone ownership is on a rapid rise across both sexes with men slightly higher and women rapidly catching up (by 2020). However, the rate of phone number change is quite high and this needs to be considered. The FRS and the FINDRISC scores are generally acceptable, however, some adaptation to match local practices and the seasonal dietary practices need to be addressed during survey planning, data analysis and interpretation.</p><p dir="ltr">Key terms: Late respondents, Mobile phones, Bias, Framingham, Finnish Diabetes Risk Score.</p><h3>List of scientific papers</h3><p dir="ltr">Paper I: Characterizing late respondents in the Rakai Community Cohort Study, 2012-2020, Uganda<b>. </b><b>Robert Ssekubugu</b>, Zangin Zeebari, Hadija Nakawooya, Ping Teresa Yeh, Anthony Ndyanabo, Fredrick Edward Makumbi, Anna Mia Ekström, Grace Nalwoga Kigozi, Eli. M Binder, Kate Grabowski, David Serwadda, Victor Ssempijja, Larry W Chang, Godfrey Kigozi, Helena Nordenstedt [Manuscript]</p><p dir="ltr">Paper II: Use of Mobile Phone to collect data on COVID-19; phone access and participation rates in Rakai, Uganda. <b>Robert Ssekubugu</b>, Anthony Ndyanabo, Fredrick Edward Makumbi, Anna Mia Ekström, Laura Bere, Grace Nalwoga Kigozi, Hadijja Nakawooya, Joseph Ssekasanvu, Maria J. Wawer, Fred Nalugoda, Nelson Sewankambo, Victor Ssempijja, Betty Nantume, David Serwadda, Godfrey Kigozi, Ronald H. Gray, Larry W Chang, M. Kate Grabowski, Helena Nordenstedt & Joseph Kagaayi Glob Health Action. 2024 Dec 31;17(1):2419160. <a href="https://doi.org/10.1080/16549716.2024.2419160" rel="noreferrer" target="_blank">https://doi.org/10.1080/16549716.2024.2419160</a></p><p dir="ltr">Paper III: Mobile phone ownership, number change, time to change and the implications to public health services delivery in Uganda, 2010-2020<b>. </b><b>Robert Ssekubugu</b>, Ping Teresa Yeh, Hadija Nakawooya, Victor Ssempijja, Godfrey Kigozi, Joseph Kagaayi, Fred Nalugoda, Anna Mia Ekström, Betty Nantume, David Serwadda, Philip Kreniske, Zangin Zeebari, Michelle A. Moffa, Larry W. Chang, Kate M. Grabowski, Fredrick Makumbi and Helena Nordenstedt [Manuscript]</p><p dir="ltr">Paper IV: Cardiovascular (Framingham) and type II diabetes (Finnish Diabetes) risk scores: a qualitative study of local knowledge of diet, physical activity and body measurements in rural Rakai, Uganda<b>. </b><b>Robert Ssekubugu</b>, Fredrick Makumbi1, Rocio Enriquez, Susanne Rautiainen Lagerstrom, Ping Teresa Yeh, Caitlin E. Kennedy, Ronald H. Gray, Lilian Negesa, David M. Serwadda, Godfrey Kigozi, Anna Mia Ekström and Helena Nordenstedt BMC Public Health. 2022 Nov 29;22(1):2214. <a href="https://doi.org/10.1186/s12889-022-14620-9" rel="noreferrer" target="_blank">https://doi.org/10.1186/s12889-022-14620-9</a></p>

Mobile phones are powerful tools to connect people. The streams of Call Detail Records (CDR’s) generating from these devices provide a powerful abstraction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale and generated in real-time, the underlying social networks are inherently complex and, thus, difficult to analyze. Conventional data analysis performed by telecom operators is slow, done by request and implies heavy costs in data warehouses. In face of these challenges, real-time streaming analysis becomes an ever increasing need to mobile operators, since it enables them to quickly detect important network events and optimize business operations. Sampling, together with visualization techniques, are required for online exploratory data analysis and event detection in such networks. In this chapter, we report the burgeoning body of research in network sampling, visualization of streaming social networks, stream analysis and the solutions proposed so far.

Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking results in improving the scalability of GNNs to work on static graphs with millions of nodes. However, how to instantly represent continuous changes of large-scale dynamic graphs with GNNs is still an open problem. Existing dynamic GNNs focus on modeling the periodic evolution of graphs, often on a snapshot basis. Such methods suffer from two drawbacks: first, there is a substantial delay for the changes in the graph to be reflected in the graph representations, resulting in losses on the model's accuracy; second, repeatedly calculating the representation matrix on the entire graph in each snapshot is predominantly time-consuming and severely limits the scalability. In this paper, we propose Instant Graph Neural Network (InstantGNN), an incremental computation approach for the graph representation matrix of dynamic graphs. Set to work with dynamic graphs with the edge-arrival model, our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Graphs with dynamic structures and dynamic attributes are both supported. The upper bounds of time complexity of those updates are also provided. Furthermore, our method provides an adaptive training strategy, which guides the model to retrain at moments when it can make the greatest performance gains. We conduct extensive experiments on several real-world and synthetic datasets. Empirical results demonstrate that our model achieves state-of-the-art accuracy while having orders-of-magnitude higher efficiency than existing methods.

We study the problem of large-scale network embedding, which aims to learn\nlatent representations for network mining applications. Previous research shows\nthat 1) popular network embedding benchmarks, such as DeepWalk, are in essence\nimplicitly factorizing a matrix with a closed form, and 2)the explicit\nfactorization of such matrix generates more powerful embeddings than existing\nmethods. However, directly constructing and factorizing this matrix---which is\ndense---is prohibitively expensive in terms of both time and space, making it\nnot scalable for large networks.\n In this work, we present the algorithm of large-scale network embedding as\nsparse matrix factorization (NetSMF). NetSMF leverages theories from spectral\nsparsification to efficiently sparsify the aforementioned dense matrix,\nenabling significantly improved efficiency in embedding learning. The\nsparsified matrix is spectrally close to the original dense one with a\ntheoretically bounded approximation error, which helps maintain the\nrepresentation power of the learned embeddings. We conduct experiments on\nnetworks of various scales and types. Results show that among both popular\nbenchmarks and factorization based methods, NetSMF is the only method that\nachieves both high efficiency and effectiveness. We show that NetSMF requires\nonly 24 hours to generate effective embeddings for a large-scale academic\ncollaboration network with tens of millions of nodes, while it would cost\nDeepWalk months and is computationally infeasible for the dense matrix\nfactorization solution. The source code of NetSMF is publicly available\n(https://github.com/xptree/NetSMF).\n

In this paper, we address job scheduling in Distributed Computing Infrastructures, that is a loosely coupled network of autonomous acting High Performance Computing systems. In contrast to the common approach of mutual workload exchange, we consider the more intuitive operator's viewpoint of load-dependent resource reconfiguration. In case of a site's over-utilization, the scheduling system is able to lease resources from other sites to keep up service quality for its local user community. Contrary, the granting of idle resources can increase utilization in times of low local workload and thus ensure higher efficiency. The evaluation considers real workload data and is done with respect to common service quality indicators. For two simple resource exchange policies and three basic setups we show the possible gain of this approach and analyze the dynamics in workload-adaptive reconfiguration behavior.

Convergence is a term that has been gaining increasing attention across the globe. Conceptually situated between technological and socio-cultural domains – as has been intensely debated between the technological and social determinists since the birth of computer mediated communication (CMC) – mobile culture is no exception. It is powered by inter-operable and multi-functional mobile telecommunication technologies, which are also becoming increasingly high functional especially via fixed-mobile convergence, interconnecting fixed and wireless broadband networks. Wide diffusion and convergence of digital communication technologies – wired and wireless – in recent years have resulted in the emergence of two key paradigmatic convergences: between technology and sociality, and between productivity and play. I will first elucidate these convergences; I will then discuss cultural characteristics of specific East Asian cultures, focusing on South Korea, China, and Japan.

Grid Computing is one of the new and innovative information technologies that attempt to make resources sharing global and more easier. Integrated in networked areas, the resources and services in grid are dynamic, heterogeneous and they belong to multiple spaced domains, which effectively enables a large scale collection, sharing and diffusion of data. However, grid computing stills a new paradigm that raises many security issues and conflicts in the computing infrastructures where it is integrated. In this paper, we propose an intrusion detection system (IDS) based on the autonomy, intelligence and independence of mobile agents to record the behaviors and actions on the grid resource nodes to detect malicious intruders. This is achieved through the use of cryptographic traces associated with chaining mechanism to elaborate hashed black statements of the executed agent code, which are then compared to depict intrusions. We have conducted experiments basing three metrics: network load, response time and detection ability to evaluate the effectiveness of our proposed IDS.

Cloud and grid computing requires new data management paradigms, new data models, systems, and capabilities. Data-intensive systems such as column-oriented database systems will play an important role in cloud data management besides traditional databases. This chapter examines column-oriented databases in virtual environments and provides evidence that they can benefit from virtualization in cloud and grid computing scenarios. The major contributions involve: (1) the experimental results show that column-oriented databases are a good fit for cloud and grid computing; (2) it is demonstrated that they offer acceptable performance and response times, as well as better usage of virtual resources. Especially for high selectivity, CPU- and memory-intensive join queries virtual performance is better than nonvirtualized performance; (3) the performance data shows that in virtual environments they make good use of parallelism and have better support for clustering (parallel execution on multiple clustered VMs is faster than on a single VM with equal resources) due to data model, read-mostly data optimizations and hypervisor-level optimizations; and (4) analysis of the architectural and system underpinning contributing to these results.